{"id":"W4404047394","doi":"10.1038/s41598-024-77018-0","title":"The impact of sex/gender-specific funding and editorial policies on biomedical research outcomes: a cross-national analysis (2000–2021)","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Korea Institute of Science and Technology Information; National Institutes of Health; Korea Institute of Science and Technology; UK Research and Innovation","keywords":"Political science; Medicine; MEDLINE; Data science; Family medicine; Computer science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.07849236,0.0002987293,0.0005717815,0.006839164,0.001187037,0.005852449,0.0009955382,0.0009199331,0.00301901],"category_scores_gemma":[0.2357415,0.0003541077,0.001050435,0.008698131,0.001877591,0.00385909,0.00238077,0.001402542,0.0006212146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004554942,"about_ca_system_score_gemma":0.01391784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02280938,"about_ca_topic_score_gemma":0.02496125,"domain_scores_codex":[0.9396,0.02551063,0.009798538,0.003506624,0.01787872,0.003705395],"domain_scores_gemma":[0.4783529,0.2323872,0.1918969,0.01313145,0.07082829,0.01340333],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001004106,0.00008712553,0.8930095,0.002272192,0.0006748951,0.0003010738,0.005265397,0.0006331343,0.0007436271,0.00444534,0.01746189,0.07410171],"study_design_scores_gemma":[0.00006157487,0.000169544,0.9425563,0.0013574,0.0002998967,0.0004509838,0.005057491,0.0004792861,0.001072528,0.001180396,0.04725349,0.0000611144],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8789156,0.03417626,0.002823272,0.03238278,0.00219919,0.0003168832,0.01892569,0.0001617676,0.03009852],"genre_scores_gemma":[0.9790445,0.006944073,0.001890045,0.003623418,0.0008097632,0.0003484688,0.003856958,0.00009177302,0.003391055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9931608,"threshold_uncertainty_score":0.4151123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1887266950470111,"score_gpt":0.5092050973096007,"score_spread":0.3204784022625896,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}